This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Power-Efficient Access-Point Selection for Indoor Location Estimation
July 2006 (vol. 18 no. 7)
pp. 877-888
An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of Access Points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet (Clustering and Decision Tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy.

[1] A. Ladd, K. Bekris, G. Marceau, A. Rudys, L. Kavraki, and D. Wallach, “Robotics-Based Location Sensing Using Wireless Ethernet,” Proc. MOBICOM2002 Conf., pp. 227-238, Sept. 2002.
[2] C. Gentile and L.K. Berndt, “Robust Location Using System Dynamics and Motion Constraints,” Proc. IEEE Conf. Comm., vol. 3, pp. 1360-1364, June 2004.
[3] M. Youssef and A. Agrawala, “Handling Samples Correlation in the Horus System,” Proc. IEEE InfoCom 2003 Conf., vol. 2, pp. 1023-1031, Mar. 2004.
[4] P. Bahl, A. Balachandran, and V. Padmanabhan, “Enhancements to the RADAR User Location and Tracking System,” technical report, Microsoft Research, Feb. 2000.
[5] L.M. Ni, Y. Liu, Y.C. Lau, and A.P. Patil, “Landmarc: Indoor Location Sensing Using Active RFID,” Proc. IEEE Int'l Conf. Pervasive Computing and Comm. 2003, pp. 407-415, Mar. 2003.
[6] D. Fox, J. Hightower, L. Liao, and D. Schulz, “Bayesian Filtering for Location Estimation,” IEEE Pervasive Computing, vol. 2, no. 3, pp. 24-33, 2002.
[7] J. Yin, X.Y. Chai, and Q. Yang, “High-Level Goal Recognition in a Wireless Lan,” Proc. 19th Nat'l Conf. Artificial Intelligence (AAAI '04), pp. 578-584, July 2004.
[8] P. Bahl and V.N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM2000 Conf., pp. 775-784, 2000.
[9] E.S. Bhasker, S.W. Brown, and W.G. Griswold, “Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing,” Proc. IEEE Int'l Conf. Pervasive Computing and Comm. 2004 (PerCom '04), pp. 111-120, Mar. 2004.
[10] M. Youssef, A. Agrawala, and U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Proc. IEEE Pervasive Computing, pp. 143-152, Mar. 2003.
[11] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” Int'l J. Wireless Information Networks, vol. 9, no. 3, pp. 155-164, July 2002.
[12] M. Youssef and A. Agrawala, “On the Optimality of WLAN Location Determination Systems,” Proc. Comm. Networks and Distributed Systems Modeling and Simulation Conf., Jan. 2004.
[13] R. Kravets and R. Krishnan, “Power Management Techniques for Mobile Communication,” Proc. Fourth Ann. ACM/IEEE Int'l Conf. Mobile Computing and Networking (MOBICOM'98), pp. 157-168, Oct. 1998.
[14] M. Stemm and R.H. Katz, “Measuring and Reducing Energy Consumption of Network Interfaces in Handheld Devices,” IEICE Trans. Fundamentals of Electronics, Comm., and Computer Science, vol. 80, no. 8, pp. 1125-1131, Aug. 1997.
[15] I. Hong and M. Potkonjak, “Power Optimization in Disk-Based Real-Time Application Specific Systems,” Proc. 1996 IEEE/ACM Int'l Conf. Computer-Aided Design, pp. 10-14, Nov. 1996.
[16] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, “DRPM: Dynamic Speed Control for Power Management in Server Class Disks,” ACM SIGARCH Computer Architecture News, vol. 31, no. 2, May 2003.
[17] M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for Reduced CPU Energy,” Proc. First USENIX Symp. Operating System Design and Implementation (OSDI), pp. 13-23, Nov. 1994.
[18] J.R. Lorch and A.J. Smith, “Scheduling Techniques for Reducing Processor Energy Use in Macos,” Wireless Networks, vol. 3, no. 5, pp. 311-324, 1997.
[19] W.-C. Lee and D.L. Lee, “Using Signature Techniques for Information Filtering in Wireless and Mobile Environments,” J. Distributed and Parallel Databases, vol. 4, no. 3, pp. 205-227, July 1996.
[20] N. Shivakumar and S. Venkatasubramanian, “Efficient Indexing for Broadcast Based Wireless Systems,” ACM/Baltzer Mobile Networks and Applications (MONET), vol. 1, no. 4, pp. 433-446, Dec. 1996.
[21] Y. Xu and W.-C. Lee, “On Localized Prediction for Power Efficient Object Tracking in Sensor Networks,” Proc. First Int'l Workshop Mobile Distributed Computing (MDC), pp. 434-439, May 2003.
[22] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” Proc. Hawaii Int'l Conf. System Sciences (HICSS), pp. 1-10, Jan. 2000.
[23] Y.Q. Xu, J. Winter, and W.-C. Lee, “Prediction-Based Strategies for Energy Saving in Object Tracking Sensor Networks,” Proc. IEEE Int'l Conf. Mobile Data Management (MDM '04), pp. 346-357, Jan. 2004.
[24] H. Hashemi, “The Indoor Radio Propagation Channel,” vol. 81, no. 7, pp. 943-968, 1993.
[25] A.W. Papers, “Power Consumption and Energy Efficiency Comparisons of WLAN Products,” technical report, Atheros Inc., 2003.
[26] J.-P. Ebert, B. Burns, and A. Wolisz, “A Trace-Based Approach for Determining the Energy Consumption of a WLAN Network Interface,” Proc. European Wireless 2002 Conf., pp. 230-236, Feb. 2002.
[27] T.E. system, http:/www.ekahau.com, 2006.
[28] B. Delaney, “Reduced Energy Consumption and Improved Accuracy for Distributed Speech Recognition in Wireless Environments,” PhD dissertation, Georgia Inst. of Tech nology, 2004.
[29] T. Mitchell, Machine Learning. McGraw-Hill, 1997.
[30] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2001.
[31] J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.

Index Terms:
Data mining in mobile wireless networks, power efficient computation.
Citation:
Yiqiang Chen, Qiang Yang, Jie Yin, Xiaoyong Chai, "Power-Efficient Access-Point Selection for Indoor Location Estimation," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 7, pp. 877-888, July 2006, doi:10.1109/TKDE.2006.112
Usage of this product signifies your acceptance of the Terms of Use.